Effects of energy loads on energy and nutrient absorption rates and gut microbiome in humans: A randomized crossover trial
Corresponding Author
Eiichi Yoshimura
Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Correspondence
Eiichi Yoshimura and Motohiko Miyachi, Kento Innovation Park Bldg 3-17 Seinrioka Shinmachi, Settsu, Osaka 566-0002, Japan.
Email: [email protected] and [email protected]
Search for more papers by this authorYuka Hamada
Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorYoichi Hatamoto
Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorTakashi Nakagata
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Department of Physical Activity Research, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorHinako Nanri
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Department of Physical Activity Research, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorYui Nakayama
Department of Physical Activity Research, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorTakanori Hayashi
Department of Clinical Nutrition, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorIppei Suzuki
Department of Food Function and Labeling, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorTakafumi Ando
Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan
Search for more papers by this authorKazuko Ishikawa-Takata
Faculty of Applied Biosciences, Tokyo University of Agriculture, Tokyo, Japan
Search for more papers by this authorShigeho Tanaka
Faculty of Nutrition, Kagawa Nutrition University, Saitama, Japan
Institute of Nutrition Sciences, Kagawa Nutrition University, Saitama, Japan
Search for more papers by this authorRei Ono
Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Department of Physical Activity Research, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorJonguk Park
Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorKoji Hosomi
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorKenji Mizuguchi
Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Institute for Protein Research, Osaka University, Osaka, Japan
Search for more papers by this authorJun Kunisawa
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorCorresponding Author
Motohiko Miyachi
Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Department of Physical Activity Research, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Faculty of Sport Sciences, Waseda University, Saitama, Japan
Correspondence
Eiichi Yoshimura and Motohiko Miyachi, Kento Innovation Park Bldg 3-17 Seinrioka Shinmachi, Settsu, Osaka 566-0002, Japan.
Email: [email protected] and [email protected]
Search for more papers by this authorCorresponding Author
Eiichi Yoshimura
Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Correspondence
Eiichi Yoshimura and Motohiko Miyachi, Kento Innovation Park Bldg 3-17 Seinrioka Shinmachi, Settsu, Osaka 566-0002, Japan.
Email: [email protected] and [email protected]
Search for more papers by this authorYuka Hamada
Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorYoichi Hatamoto
Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorTakashi Nakagata
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Department of Physical Activity Research, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorHinako Nanri
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Department of Physical Activity Research, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorYui Nakayama
Department of Physical Activity Research, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorTakanori Hayashi
Department of Clinical Nutrition, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorIppei Suzuki
Department of Food Function and Labeling, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorTakafumi Ando
Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan
Search for more papers by this authorKazuko Ishikawa-Takata
Faculty of Applied Biosciences, Tokyo University of Agriculture, Tokyo, Japan
Search for more papers by this authorShigeho Tanaka
Faculty of Nutrition, Kagawa Nutrition University, Saitama, Japan
Institute of Nutrition Sciences, Kagawa Nutrition University, Saitama, Japan
Search for more papers by this authorRei Ono
Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Department of Physical Activity Research, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorJonguk Park
Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorKoji Hosomi
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorKenji Mizuguchi
Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Institute for Protein Research, Osaka University, Osaka, Japan
Search for more papers by this authorJun Kunisawa
Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Search for more papers by this authorCorresponding Author
Motohiko Miyachi
Department of Nutrition and Metabolism, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Department of Physical Activity Research, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health, and Nutrition, Osaka, Japan
Faculty of Sport Sciences, Waseda University, Saitama, Japan
Correspondence
Eiichi Yoshimura and Motohiko Miyachi, Kento Innovation Park Bldg 3-17 Seinrioka Shinmachi, Settsu, Osaka 566-0002, Japan.
Email: [email protected] and [email protected]
Search for more papers by this authorAbstract
Objective
This study aimed to determine the effects of different energy loads on the gut microbiota composition and the rates of energy and nutrient excretion via feces and urine.
Methods
A randomized crossover dietary intervention study was conducted with three dietary conditions: overfeeding (OF), control (CON), and underfeeding (UF). Ten healthy men were subjected to each condition for 8 days (4 days and 3 nights in nonlaboratory and laboratory settings each). The effects of dietary conditions on energy excretion rates via feces and urine were assessed using a bomb calorimeter.
Results
Short-term energy loads dynamically altered the gut microbiota at the α-diversity (Shannon index), phylum, and genus levels (p < 0.05). Energy excretion rates via urine and urine plus feces decreased under OF more than under CON (urine −0.7%; p < 0.001, urine plus feces −1.9%; p = 0.049) and UF (urine −1.0%; p < 0.001, urine plus feces −2.1%; p = 0.031). However, energy excretion rates via feces did not differ between conditions.
Conclusions
Although short-term overfeeding dynamically altered the gut microbiota composition, the energy excretion rate via feces was unaffected. Energy excretion rates via urine and urine plus feces were lower under OF than under CON and UF conditions.
CONFLICT OF INTEREST STATEMENT
The authors declared no conflict of interest.
Open Research
DATA AVAILABILITY STATEMENT
The data contained in the manuscript, codebook, and analysis codes will be made available upon request following approval by the Ethics Committee.
Supporting Information
| Filename | Description |
|---|---|
| oby23935-sup-0001-FigureS1.pdfPDF document, 92.3 KB | Figure S1. Study flow diagram. |
| oby23935-sup-0002-FigureS2.pdfPDF document, 219.3 KB | Figure S2. Phylogeny of gut bacteria in intervention by subject. (A–J) denote subjects, followed by intervention conditions. |
| oby23935-sup-0003-FigureS3.pdfPDF document, 100.8 KB | Figure S3. Bar chart comparing the gut bacterial community composition at the genus level between conditions for each subject. |
| oby23935-sup-0004-FigureS4.pdfPDF document, 164.6 KB | Figure S4. Multidimensional scaling plot of the phylogenetic distance of microbial communities between groups (β-diversity). As a result of comparing each group through PERMANOVA analysis as a factor, there was no significant difference in β-diversity between groups (p = 0.145). Conversely, as there was a linear trend among the groups, a linear correlation analysis of the intervention conditions as a continuous, categorical variable confirmed a significant change in β-diversity (p = 0.036). |
| oby23935-sup-0005-FigureS5.pdfPDF document, 92.2 KB | Figure S5. Factors related to changes in the rate of energy excretion from feces. |
| oby23935-sup-0006-Appendix.docxWord 2007 document , 155 KB | Appendix S1: Supporting information. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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